Interactive Discovery and Semantic Labeling of Patterns in Spatial Data

空间数据模式的交互式发现和语义标记

• 批准号: 0937139
• 负责人: Thomas Funkhouser
• 金额: $ 49.99万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937139&HistoricalAwards=false
• 关键词: Interactive; Discovery; Semantic; Labeling; Patterns

Finding and labeling semantic patterns in large, spatial data sets is one of the most important problems facing computer scientists today. Massive spatial data sets are being acquired in almost every scientific discipline, such as medicine, geology, biology, astrophysics, and others. Finding meaningful patterns in those data is often the bottleneck to scientific discovery. The proposed research is to develop a transformative machine learning methodology, where the process of discovering semantic patterns in large spatial data sets is interactive and semi-autonomous. With the proposed tools and algorithms, the user is provided with an interactive system that shows the most likely segmentations and labelings given the information provided so far, but allows the user to provide additional information as he/she sees fit. The user might adjust a segmentation, provide a label, or specify an expected pattern. The system will adapt in real time to each of these inputs, thus adjusting its predictions throughout the data. The broad impact of the proposed plan will be enhanced through an integrated educational and outreach plan. Besides the published results of research results, the field will benefit from free distribution of research and education resources, including web pages, bibliographies, software, and data sets, including augmentations to WordNet. Further broad impacts include focused workshops and courses on shape analysis, machine learning, and visualization at both the university and professional levels. Finally, diversity enhancement programs will promote the opportunities for disadvantaged groups in research.

在大型空间数据集中发现和标记语义模式是当今计算机科学家面临的最重要的问题之一。 几乎每个科学学科都在获取大量的空间数据集，例如医学，地质学，生物学，天体物理学等。 在这些数据中找到有意义的模式往往是科学发现的瓶颈。 拟议的研究是开发一种变革性的机器学习方法，在大型空间数据集中发现语义模式的过程是交互式和半自治的。 利用所提出的工具和算法，向用户提供了交互式系统，该交互式系统示出了到目前为止所提供的给定信息的最可能的分割和标记，但是允许用户在他/她认为合适时提供附加信息。用户可以调整分割、提供标签或指定预期模式。 该系统将在真实的时间内适应这些输入中的每一个，从而在整个数据中调整其预测。 将通过一项综合教育和外联计划加强拟议计划的广泛影响。除了已发表的研究成果外，该领域还将受益于免费分发的研究和教育资源，包括网页、书目、软件和数据集，包括WordNet的扩充。 进一步的广泛影响包括在大学和专业层面上的形状分析，机器学习和可视化的重点研讨会和课程。 最后，多样性增强计划将促进弱势群体在研究中的机会。